A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
The Journal of Machine Learning Research , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.
citing papers explorer
-
A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
-
Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes
Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.